Intelligent Reflecting Surface-Aided Radar Spoofing
Electronic countermeasure (ECM) technology plays a critical role in modern electronic warfare, which can interfere with enemy radar detection systems by noise or deceptive signals. However, the conventional active jamming strategy incurs additional hardware and power costs and has the potential threat of exposing the target itself. To tackle the above challenges, we propose a new intelligent reflecting surface (IRS)-aided radar spoofing strategy, where IRS is deployed on the surface of a target to help eliminate the signals reflected towards the hostile radar to shield the target, while simultaneously redirecting its reflected signal towards a surrounding clutter to generate deceptive angle-of-arrival (AoA) sensing information for the radar. The IRS’s reflection coefficients are optimized to maximize the received signal power at the radar from the direction of the selected clutter subject to the constraint that its received power from the direction of the target is lower than a given detection threshold. We first solve this non-convex optimization problem using the semidefinite relaxation (SDR) method. A lower-complexity solution is further proposed to reduce computational burden. Simulation results validate the efficacy of our proposed IRS-aided spoofing system as compared to various benchmark schemes.
Authors
- Xiaodan Shao (ORCID: https://orcid.org/0000-0003-3249-8045)
- Haozhe Wang (ORCID: https://orcid.org/0000-0002-7621-772X)
- Beixiong Zheng (ORCID: https://orcid.org/0000-0002-2374-9263)
- Rui Zhang (ORCID: https://orcid.org/0000-0002-8729-8393)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Shenzhen Research Institute of Big Data (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- IEEE Wireless Communications Letters
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1109/lwc.2024.3442559
- Citations
- 7
- Primary Topic
- Advanced Antenna and Metasurface Technologies
- Type
- article
- Field-Weighted Citation Impact
- 9.09
Funders
- National Natural Science Foundation of China
- Guangdong Provincial Key Laboratory of Construction Foundation
- Shenzhen Research Foundation
- Shenzhen Research Institute of Big Data
- Fundamental Research Funds for the Central Universities